Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents results of an institutional survey (N = 258) at a medium-sized Austrian university with a STEM focus, supplemented with interviews (N = 18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach. The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.

中文翻译：

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在行政和研究之间：了解机构背景下的数据管理实践

研究数据管理 (RDM) 承诺使研究成果更加透明、可查找和可重复。简化跨学科数据管理的策略至关重要。本文介绍 了以 STEM 为重点的奥地利中等规模大学的机构调查 ( N = 258) 的结果，并辅以访谈 ( N = 18)，概述跨学院和学科背景的 RDM 实践的发展状况。RDM 服务正在兴起，但仍略落后于荷兰和英国等领先国家，这仅显示了与 RDM 相适应的文化的开端。在数据量、数据集的复杂性、数据收集和分析以及数据存档方面，院系和研究所之间存在相当大的差异。领域内的数据共享实践往往不一致。RDM 主要被视为一项行政任务，不利于良好研究实践的考虑。RDM 的问题分为两类：一般问题超越了特定的研究兴趣、基础设施和部门，而特定于学科的问题需要更有针对性的方法。本文通过将深入的定性材料与有关 RDM 实践和需求的量化详细数据相结合，扩展了 RDM 实践的最新技术。任何具有类似议程的可比研究机构都应该对这些发现感兴趣。